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Migrant workers, vital for urban sustainable development, often exhibit leisure-time physical inactivity (LTPI). Few studies have examined LTPI and its constraints among migrant workers. This study aimed to identify the determinants of LTPI and its constraints among migrant workers. Guangzhou was used as a case study through a questionnaire survey of 26 communities (n = 1024). Logistic regression assessed the impact of household registration on LTPI and its interaction effects. The determinants of LTPI among migrant workers were compared with those of the other groups. The study found a link between LTPI and the living environment among residents and migrant workers. Household registration influences LTPI through interactions with factors such as the number of sports facilities and community greetings. The main barriers to leisure-time physical activity among migrant workers were insufficient education, social capital, and green open spaces. This study discusses the underlying mechanisms and proposes measures to address LTPI among migrant workers.

Aims: Increasing income inequalities in leisure time physical inactivity have been reported in the relatively socially equal setting of northern Sweden. The present report seeks to contribute to the literature by exploring the contribution of different factors to the income inequalities in leisure time physical inactivity in northern Sweden. Methods: This study was based on the 2014 Health on Equal Terms survey, distributed in the four northernmost counties of Sweden. The analytical sample consisted of 21,000 respondents aged 16–84. Six thematic groups of explanatory variables were used: demographic variables, socioeconomic factors, material resources, family-, psychosocial conditions and functional limitations. Income inequalities in leisure time physical inactivity were decomposed by Wagstaff-type decomposition analysis. Results: Income inequalities in leisure time physical inactivity were found to be explained to a considerable degree by health-related limitations and unfavourable socioeconomic conditions. Material and psychosocial conditions seemed to be of moderate importance, whereas family and demographic characteristics were of minor importance. Conclusions: This study suggests that in order to achieve an economically equal leisure time physical inactivity, policy may need to target the two main barriers of functional limitations and socioeconomic disadvantages.

Objective We examined the association between job stress and leisure-time physical inactivity, adjusting for individual time-invariant attributes. Methods We used data from a Japanese occupational cohort survey, which included 31 025 observations of 9871 individuals. Focusing on the evolution of job stress and leisure-time physical inactivity within the same individual over time, we employed fixed-effects logistic models to examine the association between job stress and leisure-time physical inactivity. We compared the results with those in pooled cross-sectional models and fixed-effects ordered logistic models. Results Fixed-effects models showed that the odds ratio (OR) of physical inactivity were 22% higher for those with high strain jobs [high demands/low control; OR 1.22, 95% confidence interval (95% CI) 1.03-1.43] and 17% higher for those with active jobs (high demands/high control; OR 1.17,95% CI 1.02-1.34) than those with low strain jobs (low demands/high control). The models also showed that the odds of physical inactivity were 28% higher for those with high effort/low reward jobs (OR 1.28, 95% CI 1.10-1.50) and 24% higher for those with high effort/high reward jobs (OR 1.24, 95% CI 1.07-1.43) than those with low effort/high reward jobs. Fixed-effects ordered logistic models led to similar results. Conclusion Job stress, especially high job strain and effort-reward imbalance, was modestly associated with higher risks of physical inactivity, even after controlling for individual time-invariant attributes.

Physical inactivity is highly prevalent. Knowledge is needed of influences on inactive lifestyles. We aimed to establish whether early adult factors predict subsequent inactivity patterns in mid-adulthood. Leisure-time inactivity (activity frequency<1/week) was assessed at 33y and 50y in the 1958 British Birth cohort (N=12,271). We assessed associations of early adult (23–33y) physical status, mental function, social, family and neighbourhood circumstances with four 33–50y patterns (never inactive, persistently inactive, deteriorating or improving) using multinomial logistic regression with and without adjustment for childhood factors (e.g. social class). Inactivity prevalence was similar at 33y and 50y (∼31%), but 17% deteriorated and 18% improved with age. Factors associated with persistent vs never inactive were: limiting illness (relative risk ratio (RRR):1.21(1.04,1.42) per number of ages exposed (0,1 or 2 times across ages 23y and 33y), obesity (1.33(1.16,1.54) per number of ages exposed), height (0.93(0.89,0.98) per 5cm), depression (1.32(1.19,1.47) per number of ages exposed); education (1.28(1.20,1.38) per decrease on 5-point scale) and neighbourhood (1.59(1.37,1.86) in ‘industrial/local authority housing areas’ and 1.33(1.12,1.58) in ‘growth/metropolitan inner areas’ vs ‘suburbs, service, rural or seaside areas’). Associations were broadly similar for inactivity deterioration. Industrial/local authority housing areas (0.75(0.61,0.91)) and longer obesity exposure (0.78(0.64,0.95)) were associated with lower RRRs for improvement. Number of children was associated with improvement, although associations varied by age. Associations remained after adjustment for childhood factors. Several early adult factors are associated with inactivity persistence and deterioration; fewer with improvement. Obesity duration and neighbourhood lived in during young adulthood had long-lasting associations with inactivity patterns in mid-life.

Objective Our aim was to examine the contribution of past and current physical workload to occupational class differences in leisure-time physical inactivity. Methods Data were taken from the Finnish population-based Health 2000 Survey of employees aged ≥30 years (N=3355). We assessed physical activity during leisure time using a questionnaire and dichotomized responses to inactive versus active. Occupational class was classified into white- and blue-collar worker. Adjustments were made for current work-related factors, other measures of socioeconomic position, clinically diagnosed chronic diseases, other health behaviors, and history of physical workload. We applied sequential logistic regression to the analyses. Results Inactivity during leisure time was more common in blue-collar employees than in their white-collar counterparts [women odds ratio (OR) 1.50,95% confidence interval (95% CI) 1.12-2.00; men OR 1.66,95% CI 1.30-2.12]. These occupational differences were not due to working hours, work schedule, or chronic diseases. Among women, current job strain decreased the occupational differences in leisure-time physical inactivity slightly (OR 1.37, 95% CI 0.99-1.04). Education and household income contributed to occupational differences for men (OR 1.45, 95% CI 1.02-2.07), but had no additional effect among women. The occupation differences in leisure-time physical inactivity disappeared after adjusting for smoking and body mass index in women (OR 1.33, 95% CI 0.97-1.83) and men (OR 1.27, 95% CI 0.88-1.82) and were further attenuated after adjusting for history of physical workload among men (OR 1.07, 95% CI 0.67-1.72). Conclusion Having a long history of exposure to physical work (among men) and a high current job strain (among women) contributed to occupational class differences in leisure-time physical inactivity.

Background To identify whether changes in adult health and social factors are associated with simultaneous changes in inactivity. Methods Health, social factors and leisure-time inactivity (activity frequency < 1/week) were self-reported at 33y and 50y in the 1958 British birth cohort ( N  = 12,271). Baseline (33y) health and social factors and also patterns of change in factors 33y-to-50y were related to inactivity 33y-to-50y (never inactive, persistently inactive, deteriorating to inactivity, or improving from inactivity) using multinomial logistic regression. Results Approximately 31% were inactive at 33y and 50y; 35% changed status 33y-to-50y (17% deteriorating to inactivity, 18% improving from inactivity). Baseline poor health and obesity were associated with subsequent (33y-to-50y) inactivity; e.g. for poor health, relative risk ratios (RRRs) for deteriorating to inactivity (vs never inactive) and improving from inactivity (vs persistently inactive) were 1.38(1.16,1.64) and 0.77(0.63,0.94) respectively. Adverse changes in health and weight were associated with simultaneous adverse changes in inactivity; e.g. worsening health (vs always good/excellent health) was associated with higher risk of deteriorating to inactivity (RRR:2.20(1.85,2.62)) and lower risk of improving from inactivity (RRR:0.61(0.49,0.77)). However, improving health and weight loss were not associated with improving from inactivity. Worsening self-efficacy 33y-to-50y was associated with lower risk of improving from inactivity; there was no association between improving self-efficacy and inactivity change. Downward social mobility was not associated with deteriorating to or improving from inactivity. Changes in depression symptom level, marriage/co-habitation or parenthood 33y-to-50y were not associated with inactivity changes. No associations were observed for employment. Conclusions Associated changes in mid-life health factors with deleterious inactivity changes, highlight the importance of maintaining health, weight and self-efficacy across adulthood to deter inactivity.

Background There is growing evidence on positive effects of physical activity (PA) on mental health. However, the focus of previous research on this relationship has typically been on mental health from the perspective of mental health problems rather than from the perspective of mental wellbeing. Further, previous research has commonly focused rather on leisure time PA without evidence on the role of other domains of PA. The aim of the present cross-sectional study was to investigate the relationship between positive mental health (PMH) and different domains of PA in young Finnish men. The secondary aim was to examine the reasons for physical inactivity among individuals with a low level of PMH. Methods Positive mental health (measured with Short Warwick-Edinburgh Mental Wellbeing Scale, SWEMWBS), self-reported leisure time, occupational and commuting PA as well as reasons for physical inactivity were measured using questionnaires ( n  = 456, mean age 29 years) among young Finnish males. Logistic regression modelling was used to generate odds for low and high levels of positive mental health for different levels of PA and sociodemographic variables. Results A weak positive association between leisure time PA and PMH was found in men with a low level of PMH (OR = 0.33, 95% CI 0.13–0.86). No association was found in the domains of commuting and occupational PA. Multivariate logistic regression analysis showed lower level of leisure time PA, unemployment and being single independently predicting low level of PMH. No associations were found between any domains of PA and high level of PMH. The most common reasons for physical inactivity among men with a low level of PMH were lack of interest (28%) and unwillingness to practise sports alone (27%). Conclusions The relationship between physical activity and positive mental health seems to vary between different domains of physical activity. The findings highlight the important role of leisure time physical activity, particularly in men with a low level of positive mental health. Strategies aimed at increasing physical activity for mental health benefits should focus particularly on providing opportunities for leisure time physical activity involving social interactions for men with lower mental wellbeing.

Objectives Shift work is a risk factor for chronic diseases, and physical inactivity can have an influence on this association. We examined whether intra-individual changes in working time characteristics were associated with changes in physical inactivity and examined the risk factors for physical inactivity among shift workers in a 17-year longitudinal study cohort. Methods Study participants were 95 177 employees from the Finnish public sector. Work schedule information was based on questionnaire responses and additional register-based working time characteristics for 26 042 employees. The associations between working time characteristics and physical inactivity were examined using a fixed-effects logistic model. To investigate the risk factors for physical inactivity among shift workers, the odds ratios (OR) of worktime control and having small children were calculated. Results Compared with day work, shift work without night shifts was associated with physical inactivity among men [OR 1.38, 95% confidence interval (CI) 1.09-1.74], whereas shift work with night shifts was negatively associated with physical inactivity among women (OR 0.85, 95% CI 0.76-0.96). Register-based working time data confirmed that workers with a higher percentage of night shifts had a lower risk of physical inactivity. Having small children was associated with physical inactivity among shift workers (OR 1.47, 95% CI 1.32-1.65). Conclusions Both survey and objective working hour data revealed that workers having work schedules with night shifts were more likely to be physically active. Having small children was a risk factor for physical inactivity among shift workers.

Objective We examine the health‐related lifestyle behaviors of informal Spanish caregivers while controlling for sociodemographic characteristics. Background Informal caregiving is an essential, albeit invisible, component of any health care delivery system that results in vast savings for national economies. Nevertheless, it remains unknown whether healthy lifestyle behaviors and the subsequent well‐being of informal caregivers may compromise their ability to continue providing their essential service. Method We compared the health‐related lifestyle behaviors between informal caregivers and non‐caregivers, applying generalized estimating equations analysis. Results We observed no significant differences in self‐rated health status between caregivers and non‐caregivers. Women and men older than 44 years of age with less than 20 hours of care per week were more likely to eat fruit and engage in physical activity. Younger women caregivers (18–44 years) with less than 20 hours of care per week were also more physically active. However, younger men with less than 20 hours of care per week smoked more, and women were more likely to use alcohol. No differences were observed between non‐caregivers and caregivers with 20 or more of care per week. Conclusions Informal caregiving affects women and men equally, being hours of care per week a determinant of caregiver/non‐caregiver differences on diet, physical activity, smoking, and drinking. Implications The results from these nationally representative data suggest both a healthy and unhealthy lifestyle caregiver effect for both women and men. This effect differs on the different health‐related behaviors and is related to the amount of time devoted to care.

Background: Sedentary behavior has received increased attention as a threat to public health all around the world. A global effort has been made to avoid the spread of noncommunicable diseases (NCDs) that are associated with poor lifestyle practices, which rely on public awareness. As a result, the purpose of this study was to analyze the attitudes toward exercise, leisure activities, and sedentary behaviour among adults in Saudi Arabia. Methods: A cross-sectional study was conducted among individuals living in the Riyadh Region in Saudi Arabia. The questionnaire (26 items) used in this study was divided into four sections, and the first section comprised demographic and basic information of the respondents (6 items). The second section asked the respondents about the time spent exercising and sedentary time spent (6 items), the third section of the study comprised eight questionnaires about the frequency of sedentary activity performed during their leisure time, and the last section was about the attitude towards sedentary behavior (6 items). Descriptive and analytical statistics were done to describe the study findings. Data were analyzed using SPSS version 27. Results: The current findings revealed that 44% (n = 305) of the respondents performed exercise 1–2 days a week, and 16.7% (n = 116) never performed any exercise. Furthermore, a considerable percentage of the respondents spent >4 h in a day as sedentary. Most of the sedentary time was spent on work relating activities 62% (n = 430), followed by time spent on coffee 36.4% (n = 252), business relating activity 22.5% (n = 156), and social media 8.9% (n = 62). In this study, most of the respondents agreed that sitting for a prolonged time might negatively impact their health. Most of the respondents showed positive attitudes towards sedentary behavior. Males were statistically more likely than females to exercise 1–2 days per week (p < 0.001). Being male and being married were both significantly associated with sedentary behavior (p < 0.001). In addition, there was a significant association between participants’ sleeping status and physical activity per week, where those who slept 5–6 h often performed physical activity, indicating a significant difference (p < 0.001) than respondents who slept 7–8 or >8 h. The participant’s age was also found to have a significant association with engaging in physical exercise (p < 0.001). Conclusions: The results of this study showed that Saudi adults are highly sedentary and inactive, though knowing the harmful consequences of inactivity. Therefore, a national active living policy must be adopted to discourage inactivity and being sedentary and encourage active living in Saudi Arabia.